Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Causale Identificatie met Gerichte Acyclische Grafen (do-calculus)× | Propensity Score Matching× | |
|---|---|---|
| Vakgebied≠ | Causale inferentie | Onderzoeksstatistiek |
| Familie≠ | Regression model | Process / pipeline |
| Jaar van ontstaan≠ | 2009 | 1983 |
| Grondlegger≠ | Judea Pearl | Paul Rosenbaum and Donald Rubin |
| Type≠ | Causal identification framework | Method |
| Oorspronkelijke bron≠ | Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606 | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗ |
| Aliassen≠ | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | PSM, propensity score weighting, covariate balance |
| Verwant≠ | 5 | 3 |
| Samenvatting≠ | DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
| ScholarGateGegevensset ↗ |
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